This invention relates generally to the field of sensor networks. More particularly, this invention relates to the improvement of event sampling quality in mobile sensor networks.
Many real-life applications for sensor networks require sampling target events with sufficient resolution over both spatial and temporal dimensions. When the deployed nodes are not sufficient to fully cover the sensor field to satisfy the spatiotemporal sampling requirements of all events, nodes with intelligent mobility can be deployed to schedule the system resources efficiently for better sampling quality.
It is known that sensor mobility can be used to improve the network coverage over time and that motion planning can be used to achieve bounded sampling quality in terms of event loss probability, when location and temporal statistics of events are known a priori. However, previous approaches assume a simple event capture model, namely, that the event is binary and one sensor node is sufficient to capture the event. This model fails for many real-world scenarios, where to fully capture the spatiotemporal dynamics of events require sampling resolution along both space and time domains. In other words, a set of sensor nodes needs to cover the entire event for a sufficient period of time.
In many scenarios, such as urban rescue or battlefield surveillance, a pre-deployed static sensor network is either costly or difficult. Mobile nodes provide a much more flexible and practical deployment approach. Also, some applications (e.g., non-intrusive habitat study) prohibit the deployment of high density networks, but are well-suited to the use mobile nodes.
Robot Swarming is a related technique that is used for a large population of relatively simple agents to collectively behave based on certain rules. However, swarming robots focus on collective behavior such as dispersing into a room, following each other, and self-assembling. They are less concerned about resource scheduling into multiple tasks with differentiated benefit-cost ratios.
Another related problem is that of multi-robot task allocation (MRTA). However, MRTA focuses on the assignment of a set of tasks to a set of heterogeneous robots (in terms of their utility of accomplish a given task) so that the overall utility of performing all tasks is optimized. Existing algorithms for MRTA are either centralized or require full cooperation among robots, and are not suitable for a large population of sensor nodes.
The virtual force concept has been used for uniformly dispersing mobile nodes into a room. In the virtual force schemes, nodes are repelled from one another by a virtual force. Specific techniques are used for virtual force generation and propagation, and for node response to the virtual force.